PRETOX Text Classifier with BioBERT

A high-performance biomedical text classifier built with BioBERT to determine if a given text is related to PRETOX (Preclinical Toxicology). This tool is designed to help researchers efficiently sift through vast amounts of literature to find toxicology-relevant information.

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Key Features

• High Accuracy: Achieves 94% accuracy on the test dataset, ensuring reliable classification.
• Domain-Specific AI: Utilizes BioBERT, a language model pre-trained on biomedical text, for nuanced understanding of scientific terminology.
• Interactive Interface: A simple and intuitive web application built with Streamlit allows for easy, code-free use by researchers and domain experts.
• Reproducible: The entire training and evaluation pipeline is available in a Jupyter Notebook for full transparency and custom experimentation.

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Live Demo

Here is a preview of the interactive Streamlit application. Users can input text directly or use the provided examples to get an instant classification.

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Model Performance

The model demonstrates strong performance in distinguishing between PRETOX-related and non-related texts.

Final Test Accuracy: 94%

Classification Report

Class	Precision	Recall	F1-Score
0 (NO_PRETOX_REL)	0.97	0.93	0.95
1 (PRETOX_REL)	0.91	0.96	0.93

Training & Validation Curves

The training history shows stable learning and good generalization from training to validation data.

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Getting Started

Follow these steps to set up and run the project locally.

1. Clone the Repository

git clone https://github.com/meetptl04/pretox-classifier.git
cd pretox-classifier

2. Set Up a Virtual Environment

It is recommended to use a virtual environment to manage dependencies.

• Linux / macOS:

  python3 -m venv venv
  source venv/bin/activate

• Windows:

  python -m venv venv
  venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

4. Run the Streamlit App

streamlit run app.py

Navigate to the local URL provided in your terminal to start using the application.

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Model & Dataset

• Model: The classifier is a fine-tuned version of BioBERT (v1.1), optimized for sequence classification. To review or retrain the model, please see the NLP_BioBert_PRETOX_REL.ipynb notebook.
• Dataset: The model was trained on the pretoxtm-dataset from Hugging Face, which contains biomedical text excerpts labeled for toxicology relevance.

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Project Structure

BioBert_app/
├── data/                    # Dataset files
├── model/                   # Trained model checkpoints
├── images/                  # Project images and screenshots
│   ├── accuracy.png
│   └── streamlit_app_image.png
├── venv/                    # Virtual environment (ignored by git)
├── app.py                   # Streamlit application source code
├── requirements.txt         # Required Python packages
└── NLP_BioBert_PRETOX_REL.ipynb # Notebook with training & evaluation code

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